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Wavelet change‐point estimation for long memory non‐parametric random design models
Author(s) -
Wang Lihong,
Cai Haiyan
Publication year - 2010
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/j.1467-9892.2009.00646.x
Subject(s) - mathematics , estimation , wavelet , parametric statistics , long memory , econometrics , statistics , parametric model , algorithm , artificial intelligence , computer science , volatility (finance) , management , economics
For a random design regression model with long memory design and long memory errors, we consider the problem of detecting a change point for sharp cusp or jump discontinuity in the regression function. Using the wavelet methods, we obtain estimators for the change point, the jump size and the regression function. The strong consistencies of these estimators are given in terms of convergence rates.